/*
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 2 of the License, or
 * (at your option) any later version.
 * 
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 * 
 * You should have received a copy of the GNU General Public License
 * along with this program; if not, write to the Free Software
 * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 * GeneticSearch.java
 * Copyright (C) 2004 University of Waikato, Hamilton, New Zealand
 * 
 */

package weka.classifiers.bayes.net.search.local;

import weka.classifiers.bayes.BayesNet;
import weka.classifiers.bayes.net.ParentSet;
import weka.core.Instances;
import weka.core.Option;
import weka.core.RevisionHandler;
import weka.core.RevisionUtils;
import weka.core.Utils;

import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;

/**
 * <!-- globalinfo-start --> This Bayes Network learning algorithm uses genetic
 * search for finding a well scoring Bayes network structure. Genetic search
 * works by having a population of Bayes network structures and allow them to
 * mutate and apply cross over to get offspring. The best network structure
 * found during the process is returned.
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -L &lt;integer&gt;
 *  Population size
 * </pre>
 * 
 * <pre>
 * -A &lt;integer&gt;
 *  Descendant population size
 * </pre>
 * 
 * <pre>
 * -U &lt;integer&gt;
 *  Number of runs
 * </pre>
 * 
 * <pre>
 * -M
 *  Use mutation.
 *  (default true)
 * </pre>
 * 
 * <pre>
 * -C
 *  Use cross-over.
 *  (default true)
 * </pre>
 * 
 * <pre>
 * -O
 *  Use tournament selection (true) or maximum subpopulatin (false).
 *  (default false)
 * </pre>
 * 
 * <pre>
 * -R &lt;seed&gt;
 *  Random number seed
 * </pre>
 * 
 * <pre>
 * -mbc
 *  Applies a Markov Blanket correction to the network structure, 
 *  after a network structure is learned. This ensures that all 
 *  nodes in the network are part of the Markov blanket of the 
 *  classifier node.
 * </pre>
 * 
 * <pre>
 * -S [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES]
 *  Score type (BAYES, BDeu, MDL, ENTROPY and AIC)
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @author Remco Bouckaert (rrb@xm.co.nz)
 * @version $Revision: 1.5 $
 */
public class GeneticSearch extends LocalScoreSearchAlgorithm {

	/** for serialization */
	static final long serialVersionUID = -7037070678911459757L;

	/** number of runs **/
	int m_nRuns = 10;

	/** size of population **/
	int m_nPopulationSize = 10;

	/** size of descendant population **/
	int m_nDescendantPopulationSize = 100;

	/** use cross-over? **/
	boolean m_bUseCrossOver = true;

	/** use mutation? **/
	boolean m_bUseMutation = true;

	/** use tournament selection or take best sub-population **/
	boolean m_bUseTournamentSelection = false;

	/** random number seed **/
	int m_nSeed = 1;

	/** random number generator **/
	Random m_random = null;

	/**
	 * used in BayesNetRepresentation for efficiently determining whether a
	 * number is square
	 */
	static boolean[] g_bIsSquare;

	class BayesNetRepresentation implements RevisionHandler {

		/** number of nodes in network **/
		int m_nNodes = 0;

		/**
		 * bit representation of parent sets m_bits[iTail + iHead * m_nNodes]
		 * represents arc iTail->iHead
		 */
		boolean[] m_bits;

		/** score of represented network structure **/
		double m_fScore = 0.0f;

		/**
		 * return score of represented network structure
		 * 
		 * @return the score
		 */
		public double getScore() {
			return m_fScore;
		} // getScore

		/**
		 * c'tor
		 * 
		 * @param nNodes
		 *            the number of nodes
		 */
		BayesNetRepresentation(int nNodes) {
			m_nNodes = nNodes;
		} // c'tor

		/**
		 * initialize with a random structure by randomly placing m_nNodes arcs.
		 */
		public void randomInit() {
			do {
				m_bits = new boolean[m_nNodes * m_nNodes];
				for (int i = 0; i < m_nNodes; i++) {
					int iPos;
					do {
						iPos = m_random.nextInt(m_nNodes * m_nNodes);
					} while (isSquare(iPos));
					m_bits[iPos] = true;
				}
			} while (hasCycles());
			calcScore();
		}

		/**
		 * calculate score of current network representation As a side effect,
		 * the parent sets are set
		 */
		void calcScore() {
			// clear current network
			for (int iNode = 0; iNode < m_nNodes; iNode++) {
				ParentSet parentSet = m_BayesNet.getParentSet(iNode);
				while (parentSet.getNrOfParents() > 0) {
					parentSet.deleteLastParent(m_BayesNet.m_Instances);
				}
			}
			// insert arrows
			for (int iNode = 0; iNode < m_nNodes; iNode++) {
				ParentSet parentSet = m_BayesNet.getParentSet(iNode);
				for (int iNode2 = 0; iNode2 < m_nNodes; iNode2++) {
					if (m_bits[iNode2 + iNode * m_nNodes]) {
						parentSet.addParent(iNode2, m_BayesNet.m_Instances);
					}
				}
			}
			// calc score
			m_fScore = 0.0;
			for (int iNode = 0; iNode < m_nNodes; iNode++) {
				m_fScore += calcNodeScore(iNode);
			}
		} // calcScore

		/**
		 * check whether there are cycles in the network
		 * 
		 * @return true if a cycle is found, false otherwise
		 */
		public boolean hasCycles() {
			// check for cycles
			boolean[] bDone = new boolean[m_nNodes];
			for (int iNode = 0; iNode < m_nNodes; iNode++) {

				// find a node for which all parents are 'done'
				boolean bFound = false;

				for (int iNode2 = 0; !bFound && iNode2 < m_nNodes; iNode2++) {
					if (!bDone[iNode2]) {
						boolean bHasNoParents = true;
						for (int iParent = 0; iParent < m_nNodes; iParent++) {
							if (m_bits[iParent + iNode2 * m_nNodes]
									&& !bDone[iParent]) {
								bHasNoParents = false;
							}
						}
						if (bHasNoParents) {
							bDone[iNode2] = true;
							bFound = true;
						}
					}
				}
				if (!bFound) {
					return true;
				}
			}
			return false;
		} // hasCycles

		/**
		 * create clone of current object
		 * 
		 * @return cloned object
		 */
		BayesNetRepresentation copy() {
			BayesNetRepresentation b = new BayesNetRepresentation(m_nNodes);
			b.m_bits = new boolean[m_bits.length];
			for (int i = 0; i < m_nNodes * m_nNodes; i++) {
				b.m_bits[i] = m_bits[i];
			}
			b.m_fScore = m_fScore;
			return b;
		} // copy

		/**
		 * Apply mutation operation to BayesNet Calculate score and as a side
		 * effect sets BayesNet parent sets.
		 */
		void mutate() {
			// flip a bit
			do {
				int iBit;
				do {
					iBit = m_random.nextInt(m_nNodes * m_nNodes);
				} while (isSquare(iBit));

				m_bits[iBit] = !m_bits[iBit];
			} while (hasCycles());

			calcScore();
		} // mutate

		/**
		 * Apply cross-over operation to BayesNet Calculate score and as a side
		 * effect sets BayesNet parent sets.
		 * 
		 * @param other
		 *            BayesNetRepresentation to cross over with
		 */
		void crossOver(BayesNetRepresentation other) {
			boolean[] bits = new boolean[m_bits.length];
			for (int i = 0; i < m_bits.length; i++) {
				bits[i] = m_bits[i];
			}
			int iCrossOverPoint = m_bits.length;
			do {
				// restore to original state
				for (int i = iCrossOverPoint; i < m_bits.length; i++) {
					m_bits[i] = bits[i];
				}
				// take all bits from cross-over points onwards
				iCrossOverPoint = m_random.nextInt(m_bits.length);
				for (int i = iCrossOverPoint; i < m_bits.length; i++) {
					m_bits[i] = other.m_bits[i];
				}
			} while (hasCycles());
			calcScore();
		} // crossOver

		/**
		 * check if number is square and initialize g_bIsSquare structure if
		 * necessary
		 * 
		 * @param nNum
		 *            number to check (should be below m_nNodes * m_nNodes)
		 * @return true if number is square
		 */
		boolean isSquare(int nNum) {
			if (g_bIsSquare == null || g_bIsSquare.length < nNum) {
				g_bIsSquare = new boolean[m_nNodes * m_nNodes];
				for (int i = 0; i < m_nNodes; i++) {
					g_bIsSquare[i * m_nNodes + i] = true;
				}
			}
			return g_bIsSquare[nNum];
		} // isSquare

		/**
		 * Returns the revision string.
		 * 
		 * @return the revision
		 */
		public String getRevision() {
			return RevisionUtils.extract("$Revision: 1.5 $");
		}
	} // class BayesNetRepresentation

	/**
	 * search determines the network structure/graph of the network with a
	 * genetic search algorithm.
	 * 
	 * @param bayesNet
	 *            the network to use
	 * @param instances
	 *            the data to use
	 * @throws Exception
	 *             if population size doesn fit or neither cross-over or
	 *             mutation was chosen
	 */
	protected void search(BayesNet bayesNet, Instances instances)
			throws Exception {
		// sanity check
		if (getDescendantPopulationSize() < getPopulationSize()) {
			throw new Exception(
					"Descendant PopulationSize should be at least Population Size");
		}
		if (!getUseCrossOver() && !getUseMutation()) {
			throw new Exception(
					"At least one of mutation or cross-over should be used");
		}

		m_random = new Random(m_nSeed);

		// keeps track of best structure found so far
		BayesNet bestBayesNet;
		// keeps track of score pf best structure found so far
		double fBestScore = 0.0;
		for (int iAttribute = 0; iAttribute < instances.numAttributes(); iAttribute++) {
			fBestScore += calcNodeScore(iAttribute);
		}

		// initialize bestBayesNet
		bestBayesNet = new BayesNet();
		bestBayesNet.m_Instances = instances;
		bestBayesNet.initStructure();
		copyParentSets(bestBayesNet, bayesNet);

		// initialize population
		BayesNetRepresentation[] population = new BayesNetRepresentation[getPopulationSize()];
		for (int i = 0; i < getPopulationSize(); i++) {
			population[i] = new BayesNetRepresentation(
					instances.numAttributes());
			population[i].randomInit();
			if (population[i].getScore() > fBestScore) {
				copyParentSets(bestBayesNet, bayesNet);
				fBestScore = population[i].getScore();

			}
		}

		// go do the search
		for (int iRun = 0; iRun < m_nRuns; iRun++) {
			// create descendants
			BayesNetRepresentation[] descendantPopulation = new BayesNetRepresentation[getDescendantPopulationSize()];
			for (int i = 0; i < getDescendantPopulationSize(); i++) {
				descendantPopulation[i] = population[m_random
						.nextInt(getPopulationSize())].copy();
				if (getUseMutation()) {
					if (getUseCrossOver() && m_random.nextBoolean()) {
						descendantPopulation[i].crossOver(population[m_random
								.nextInt(getPopulationSize())]);
					} else {
						descendantPopulation[i].mutate();
					}
				} else {
					// use crossover
					descendantPopulation[i].crossOver(population[m_random
							.nextInt(getPopulationSize())]);
				}

				if (descendantPopulation[i].getScore() > fBestScore) {
					copyParentSets(bestBayesNet, bayesNet);
					fBestScore = descendantPopulation[i].getScore();
				}
			}
			// select new population
			boolean[] bSelected = new boolean[getDescendantPopulationSize()];
			for (int i = 0; i < getPopulationSize(); i++) {
				int iSelected = 0;
				if (m_bUseTournamentSelection) {
					// use tournament selection
					iSelected = m_random.nextInt(getDescendantPopulationSize());
					while (bSelected[iSelected]) {
						iSelected = (iSelected + 1)
								% getDescendantPopulationSize();
					}
					int iSelected2 = m_random
							.nextInt(getDescendantPopulationSize());
					while (bSelected[iSelected2]) {
						iSelected2 = (iSelected2 + 1)
								% getDescendantPopulationSize();
					}
					if (descendantPopulation[iSelected2].getScore() > descendantPopulation[iSelected]
							.getScore()) {
						iSelected = iSelected2;
					}
				} else {
					// find best scoring network in population
					while (bSelected[iSelected]) {
						iSelected++;
					}
					double fScore = descendantPopulation[iSelected].getScore();
					for (int j = 0; j < getDescendantPopulationSize(); j++) {
						if (!bSelected[j]
								&& descendantPopulation[j].getScore() > fScore) {
							fScore = descendantPopulation[j].getScore();
							iSelected = j;
						}
					}
				}
				population[i] = descendantPopulation[iSelected];
				bSelected[iSelected] = true;
			}
		}

		// restore current network to best network
		copyParentSets(bayesNet, bestBayesNet);

		// free up memory
		bestBayesNet = null;
	} // search

	/**
	 * copyParentSets copies parent sets of source to dest BayesNet
	 * 
	 * @param dest
	 *            destination network
	 * @param source
	 *            source network
	 */
	void copyParentSets(BayesNet dest, BayesNet source) {
		int nNodes = source.getNrOfNodes();
		// clear parent set first
		for (int iNode = 0; iNode < nNodes; iNode++) {
			dest.getParentSet(iNode).copy(source.getParentSet(iNode));
		}
	} // CopyParentSets

	/**
	 * @return number of runs
	 */
	public int getRuns() {
		return m_nRuns;
	} // getRuns

	/**
	 * Sets the number of runs
	 * 
	 * @param nRuns
	 *            The number of runs to set
	 */
	public void setRuns(int nRuns) {
		m_nRuns = nRuns;
	} // setRuns

	/**
	 * Returns an enumeration describing the available options.
	 * 
	 * @return an enumeration of all the available options.
	 */
	public Enumeration listOptions() {
		Vector newVector = new Vector(7);

		newVector.addElement(new Option("\tPopulation size", "L", 1,
				"-L <integer>"));
		newVector.addElement(new Option("\tDescendant population size", "A", 1,
				"-A <integer>"));
		newVector.addElement(new Option("\tNumber of runs", "U", 1,
				"-U <integer>"));
		newVector.addElement(new Option("\tUse mutation.\n\t(default true)",
				"M", 0, "-M"));
		newVector.addElement(new Option("\tUse cross-over.\n\t(default true)",
				"C", 0, "-C"));
		newVector
				.addElement(new Option(
						"\tUse tournament selection (true) or maximum subpopulatin (false).\n\t(default false)",
						"O", 0, "-O"));
		newVector.addElement(new Option("\tRandom number seed", "R", 1,
				"-R <seed>"));

		Enumeration enu = super.listOptions();
		while (enu.hasMoreElements()) {
			newVector.addElement(enu.nextElement());
		}
		return newVector.elements();
	} // listOptions

	/**
	 * Parses a given list of options.
	 * <p/>
	 * 
	 * <!-- options-start --> Valid options are:
	 * <p/>
	 * 
	 * <pre>
	 * -L &lt;integer&gt;
	 *  Population size
	 * </pre>
	 * 
	 * <pre>
	 * -A &lt;integer&gt;
	 *  Descendant population size
	 * </pre>
	 * 
	 * <pre>
	 * -U &lt;integer&gt;
	 *  Number of runs
	 * </pre>
	 * 
	 * <pre>
	 * -M
	 *  Use mutation.
	 *  (default true)
	 * </pre>
	 * 
	 * <pre>
	 * -C
	 *  Use cross-over.
	 *  (default true)
	 * </pre>
	 * 
	 * <pre>
	 * -O
	 *  Use tournament selection (true) or maximum subpopulatin (false).
	 *  (default false)
	 * </pre>
	 * 
	 * <pre>
	 * -R &lt;seed&gt;
	 *  Random number seed
	 * </pre>
	 * 
	 * <pre>
	 * -mbc
	 *  Applies a Markov Blanket correction to the network structure, 
	 *  after a network structure is learned. This ensures that all 
	 *  nodes in the network are part of the Markov blanket of the 
	 *  classifier node.
	 * </pre>
	 * 
	 * <pre>
	 * -S [BAYES|MDL|ENTROPY|AIC|CROSS_CLASSIC|CROSS_BAYES]
	 *  Score type (BAYES, BDeu, MDL, ENTROPY and AIC)
	 * </pre>
	 * 
	 * <!-- options-end -->
	 * 
	 * @param options
	 *            the list of options as an array of strings
	 * @throws Exception
	 *             if an option is not supported
	 */
	public void setOptions(String[] options) throws Exception {
		String sPopulationSize = Utils.getOption('L', options);
		if (sPopulationSize.length() != 0) {
			setPopulationSize(Integer.parseInt(sPopulationSize));
		}
		String sDescendantPopulationSize = Utils.getOption('A', options);
		if (sDescendantPopulationSize.length() != 0) {
			setDescendantPopulationSize(Integer
					.parseInt(sDescendantPopulationSize));
		}
		String sRuns = Utils.getOption('U', options);
		if (sRuns.length() != 0) {
			setRuns(Integer.parseInt(sRuns));
		}
		String sSeed = Utils.getOption('R', options);
		if (sSeed.length() != 0) {
			setSeed(Integer.parseInt(sSeed));
		}
		setUseMutation(Utils.getFlag('M', options));
		setUseCrossOver(Utils.getFlag('C', options));
		setUseTournamentSelection(Utils.getFlag('O', options));

		super.setOptions(options);
	} // setOptions

	/**
	 * Gets the current settings of the search algorithm.
	 * 
	 * @return an array of strings suitable for passing to setOptions
	 */
	public String[] getOptions() {
		String[] superOptions = super.getOptions();
		String[] options = new String[11 + superOptions.length];
		int current = 0;

		options[current++] = "-L";
		options[current++] = "" + getPopulationSize();

		options[current++] = "-A";
		options[current++] = "" + getDescendantPopulationSize();

		options[current++] = "-U";
		options[current++] = "" + getRuns();

		options[current++] = "-R";
		options[current++] = "" + getSeed();

		if (getUseMutation()) {
			options[current++] = "-M";
		}
		if (getUseCrossOver()) {
			options[current++] = "-C";
		}
		if (getUseTournamentSelection()) {
			options[current++] = "-O";
		}

		// insert options from parent class
		for (int iOption = 0; iOption < superOptions.length; iOption++) {
			options[current++] = superOptions[iOption];
		}

		// Fill up rest with empty strings, not nulls!
		while (current < options.length) {
			options[current++] = "";
		}
		return options;
	} // getOptions

	/**
	 * @return whether cross-over is used
	 */
	public boolean getUseCrossOver() {
		return m_bUseCrossOver;
	}

	/**
	 * @return whether mutation is used
	 */
	public boolean getUseMutation() {
		return m_bUseMutation;
	}

	/**
	 * @return descendant population size
	 */
	public int getDescendantPopulationSize() {
		return m_nDescendantPopulationSize;
	}

	/**
	 * @return population size
	 */
	public int getPopulationSize() {
		return m_nPopulationSize;
	}

	/**
	 * @param bUseCrossOver
	 *            sets whether cross-over is used
	 */
	public void setUseCrossOver(boolean bUseCrossOver) {
		m_bUseCrossOver = bUseCrossOver;
	}

	/**
	 * @param bUseMutation
	 *            sets whether mutation is used
	 */
	public void setUseMutation(boolean bUseMutation) {
		m_bUseMutation = bUseMutation;
	}

	/**
	 * @return whether Tournament Selection (true) or Maximum Sub-Population
	 *         (false) should be used
	 */
	public boolean getUseTournamentSelection() {
		return m_bUseTournamentSelection;
	}

	/**
	 * @param bUseTournamentSelection
	 *            sets whether Tournament Selection or Maximum Sub-Population
	 *            should be used
	 */
	public void setUseTournamentSelection(boolean bUseTournamentSelection) {
		m_bUseTournamentSelection = bUseTournamentSelection;
	}

	/**
	 * @param iDescendantPopulationSize
	 *            sets descendant population size
	 */
	public void setDescendantPopulationSize(int iDescendantPopulationSize) {
		m_nDescendantPopulationSize = iDescendantPopulationSize;
	}

	/**
	 * @param iPopulationSize
	 *            sets population size
	 */
	public void setPopulationSize(int iPopulationSize) {
		m_nPopulationSize = iPopulationSize;
	}

	/**
	 * @return random number seed
	 */
	public int getSeed() {
		return m_nSeed;
	} // getSeed

	/**
	 * Sets the random number seed
	 * 
	 * @param nSeed
	 *            The number of the seed to set
	 */
	public void setSeed(int nSeed) {
		m_nSeed = nSeed;
	} // setSeed

	/**
	 * This will return a string describing the classifier.
	 * 
	 * @return The string.
	 */
	public String globalInfo() {
		return "This Bayes Network learning algorithm uses genetic search for finding a well scoring "
				+ "Bayes network structure. Genetic search works by having a population of Bayes network structures "
				+ "and allow them to mutate and apply cross over to get offspring. The best network structure "
				+ "found during the process is returned.";
	} // globalInfo

	/**
	 * @return a string to describe the Runs option.
	 */
	public String runsTipText() {
		return "Sets the number of generations of Bayes network structure populations.";
	} // runsTipText

	/**
	 * @return a string to describe the Seed option.
	 */
	public String seedTipText() {
		return "Initialization value for random number generator."
				+ " Setting the seed allows replicability of experiments.";
	} // seedTipText

	/**
	 * @return a string to describe the Population Size option.
	 */
	public String populationSizeTipText() {
		return "Sets the size of the population of network structures that is selected each generation.";
	} // populationSizeTipText

	/**
	 * @return a string to describe the Descendant Population Size option.
	 */
	public String descendantPopulationSizeTipText() {
		return "Sets the size of the population of descendants that is created each generation.";
	} // descendantPopulationSizeTipText

	/**
	 * @return a string to describe the Use Mutation option.
	 */
	public String useMutationTipText() {
		return "Determines whether mutation is allowed. Mutation flips a bit in the bit "
				+ "representation of the network structure. At least one of mutation or cross-over "
				+ "should be used.";
	} // useMutationTipText

	/**
	 * @return a string to describe the Use Cross-Over option.
	 */
	public String useCrossOverTipText() {
		return "Determines whether cross-over is allowed. Cross over combined the bit "
				+ "representations of network structure by taking a random first k bits of one"
				+ "and adding the remainder of the other. At least one of mutation or cross-over "
				+ "should be used.";
	} // useCrossOverTipText

	/**
	 * @return a string to describe the Use Tournament Selection option.
	 */
	public String useTournamentSelectionTipText() {
		return "Determines the method of selecting a population. When set to true, tournament "
				+ "selection is used (pick two at random and the highest is allowed to continue). "
				+ "When set to false, the top scoring network structures are selected.";
	} // useTournamentSelectionTipText

	/**
	 * Returns the revision string.
	 * 
	 * @return the revision
	 */
	public String getRevision() {
		return RevisionUtils.extract("$Revision: 1.5 $");
	}
} // GeneticSearch
